Analytics and AI Monthly: Vol. II – Maintenance Fee Management

Wednesday, July 11, 2018

This month we will tackle a topic near and dear to the hearts of anyone who manages a reasonable sized portfolio – Maintenance Fee Management.  Every issued patent carries ongoing maintenance fees, which are currently $1,600, $3,600 and $7,400, at 3.5, 7.5 and 11.5 years, respectively.  That means that every patent in a portfolio, over the life of the patent, will incur at least $12,600 in fees.  So, if you have a portfolio of 28,000 US grants, like Google does, you can expect to pay a total of $352,000,000 in maintenance fees over the life of the portfolio.  That does not include any other costs, and is for the US versions of the cases only.  Clearly, controlling these fees is an important job of any portfolio manager.

This article will lay out the broad strokes of the concept, which should be enough to provide an understanding sufficient to consider the proposed approach.  Next month, we will follow up with a secondary article that goes deeper into data-considerations, and which includes some follow-on strategies such as data-backed asset disposition decisions.

1. Strategic Abandonment Impediments

The typical process for managing maintenance fees involves determining some subset of cases to strategically abandon.  This carries two potential financial problems:

  • There can be considerable time involved in reviewing cases to determine if the claims or the technology of the case has merit. Google (under the parent Alphabet) has 1,163 cases on which fees are due next quarter.  Over the next 16 quarters, they would have to review between 1200 and 1500 cases, on a quarterly basis.  That is approximately 5400 cases annually which need to be reviewed; and
  • No one wants to abandon the windfall patent. This is a fear of everyone who ever abandoned a patent without fully vetting every aspect of the case.  That someday, somehow, the abandoned claims would turn out to be worth tens or hundreds of millions, and the responsible attorney is going to have some tough questions to answer.

The problem we often see however is that the fear associated with (2) drives increased cost associated with (1), or simply causes a company to “freeze” entirely, abandoning no cases, or only a very limited number (less than 1%) of cases.  Even Google/Alphabet abandoned virtually no cases until 2015 (fewer than 15 a year).  In 2015 and 2016 however, Google abandoned 521 and 638 cases respectively.  This was a drastic, but possibly necessary, step that, among other things, helped significantly curb maintenance fees.


2. The Dollars and Cents of Abandonment

To ensure everyone fully understands the value proposition I will describe herein, I will include a small amount of math below.  Below is a hypothetical review cost and average savings/case, so that we have a framework to discuss how a structured, data-back fee management approach can be both effective and cost-saving.

  • Savings

First, for the sake of example, we will assume that a company will evenly abandon across the three fee tranches (3.5, 7.5 and 11.5).  This means that the average case abandoned will have $4200 in immediate savings (the average immediate cost) and $6133 in long-term savings (because 3.5 and 7.5 year cases have additional fees due later).

So, if Google abandoned those 1200 cases evenly across tranches, Google achieved an immediate savings of $5 Million over the two years, and a long-term additional savings of $7.4 Million.  $12 Million in savings is a very compelling reason to engage in strategic abandonment.

In a quarterly sense, we frequently see abandonment rates around 3-7%, depending on industry.  We will use 5% as a target for the example, which means going forward, Google can target around 70 cases per quarter for abandonment.  This will save Google around $300k per quarter in fees, and another $425k in forward-savings, per quarter.  That makes the value proposition to Google just shy of $3 Million in annual savings.

  • Cost

Second, I will peg a hypothetical review cost of $150 to a given case.  This would be .5 hours of time at $300/hr, and is probably sufficient time to review a case for the purpose of making an abandonment decision.

As noted, Google will have to review about 1400 patents per quarter.  If Google were paying $150 per case (.5 hours of review), this would create a quarterly cost of $210k, which means that almost 75% of the immediate savings achieved from abandonment would be consumed in review-cost.  For Google, at an annual cost of over $800k, this significantly impacts the value of strategic abandonment, and means that the annual budget needle (accounting for present fees and present attorney costs) will barely move.


3. The Real Fee Problems

  • Waste 

As many readers have probably realized from experience, the real problem with maintenance fee review is waste.  If one is abandoning 5% of one’s cases, then one is paying 95% of the review cost for decisions to keep cases.

  • Justification 

Another problem, even if cases are reviewed by competent counsel, is having a record of “why” a case was abandoned.  It is unlikely that anyone will complain if a poor case or two is kept, as long as abandonment targets are being met.  It is far more likely, however, that abandonment of a valuable asset will result in protracted scrutiny, even if the value was not apparent until years after the asset was abandoned.  In such an instance, the evidence that “we had a competent attorney review this case, and it did not appear to be valuable,” may be deemed insufficient in hindsight.


4. Solving It All – Analytics to the Rescue

Working under the framework and understanding laid out above, I now will propose a data-backed, analytics (and possibly AI) managed approach to solving both of the above problems with strategic abandonment.

The two goals are simple – reduce the cost of review and provide evidence and support as to why a given case was kept or, more importantly, abandoned.  I will use the vocabulary set forth in Vol. I, relating to Facts, Inquiries and Opportunities.  Again, to summarize: Facts are things that we can discover through analytics (and AI, as I will explain), Inquiries are questions to ask in order to take advantage of the Opportunity, and Opportunities summarize what you can do after finding the facts and completing the Inquiries.

  • The Formula

Facts –

Your case was used in a 102 or 103 rejection.

Your case was used in a rejection of a competitor’s case.

Your case was filed in a CPC class in which competitors file heavily/lightly.

Your case focuses on technology types which are important/disfavored under your current strategic plan.


Inquiry(s) –

The Primary Inquiry here is “Why would I keep a case?” (note: This is not “why would I abandon a case?” as one might expect).

Some examples of answers/secondary inquiries might be:

Was the case used in a 102/103 rejection?

Was the case cited against a competitor?

Does the case cover a production-level implementation?

Is the case in a highly contested technology area?


Using parameterized, searchable metrics, reduce the size of the pile for review, by quickly and automatically setting aside any case with one or more “keeper” characteristics.  Because this case has a reason to be kept, there is no need to pay anyone to review the subject matter or claims, and each case set aside in this manner represents a review savings of $150 (using our example numbers).

  •  In Practice

Again, I must stress that even though the eventual decision with regards to maintenance fees is whether to abandon a case, the immediate decision to be made with the assistance of analytics and/or AI is whether to keep a case.

It would be inadvisable to abandon a particular case without having an actual attorney review the case, so what I am trying to achieve, instead, is a reduction in the time and money spent in having attorneys review a full portfolio, when approximately 19 out of 20 cases will be kept.

I achieve the above-stated goal by assembling a list of parameters as to why a client would keep a case, and so for example purposes we will pick four:

  • The case was used in a 102 rejection.
  • The case was used in a 103 rejection of a competitor.
  • The case was filed in a CPC class where we have maintained or increased filings over the last five years.
  • The case was filed in a CPC class where at least two competitors filed 10 or more cases each of the last 3 years.

Before I go further, I would point out that the level of granularity and specificity for the above parameters likely varies with the volume of cases you intend to abandon in a given quarter.  Remember, you are only abandoning 1 out of 20 cases in this example, so you are really looking for a “worst of the worst” type scenario.  Because of this, reasons to keep a case can be fairly vague and encompassing.  Essentially, you are saying “any case that has at least one of the above characteristics probably is not the worst case in the pile of 20, and any case that has none of the above characteristics is a good possible candidate for being the worst.”

If you wanted to abandon 20 or 30 percent of your cases, to significantly reduce cost, you would likely need to be much more specific about the details of what automatically qualifies as a “keeper.”  I do not recommend this approach as a one-off measure, however, unless you are going to review a block of many years of cases.  Otherwise, you are presupposing that a disproportionate number of cases worth abandoning are contained in the smaller quarterly or annual block of cases being presently considered.  I have an alternative approach if this tactic is desired, which I will happily discuss if you want to reach out to me individually.

Continuing with our example, we will “vet” Google’s upcoming Q3 2018 fees (1163 cases) using the first two of the above parameters.

  • Keeping cases used in any 102 rejection removes 329 cases from consideration, leaving a remainder of 834. Using our model, that is a review-cost savings of $49,350.  This also has the added advantage of ensuring Google does not abandon any case cited as 102 art.
  • Keeping cases cited against Microsoft, Apple, IBM or Qualcomm (arbitrary competitors) leaves a remainder of 725 cases. This represents a savings of $16,305 and ensures that any cases cited against those competitors, for any reason, are not abandoned.

The remaining two inquiries are a bit more complicated, but easily achievable with a good analytics tool, such as AcclaimIP ®.  For the sake of expediency, let’s assume that each additional sort set aside another 20 percent of the remaining cases, saving another $39,000 and leaving a final pile of 464 cases.

That remaining pile then represents all cases which have none of the identified “keeper” characteristics, and that is the pile that would then be reviewed by attorneys.  Further sorting could be done at this time, if the pile were not small enough, following the same model with additional parameters.

This “keeper” sort achieves a review-cost savings of around $105,000.  It also ensures that no cases will be abandoned which have any of the identified characteristics.  This means that later, if the question as to why a case was abandoned is asked, not only will an attorney have reviewed and approved the case for abandonment, but there will also be a handful of quantified, provable metrics that were not met by the case at the time of abandonment.



We at Brooks Kushman believe that this model provides a cost-effective approach to the abandonment problem in a manner that provides solid data for the decision to keep or abandon a given matter.  The savings achieved by strategic abandonment of even just 5% can be significant over time, and the cost and time involved in using modern analytics to pre-sort your cases is marginal by comparison to both the savings and a more traditional model of reviewing every case.  Because of the value-added data points that “prove” why a case was or was not good, we highly recommend this sort of structured consideration approach.

A second part to this article will be published by the end of July, and that part will focus on some of the more complex analytics usable for maintenance fee management.